{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calcAB(x, y):\n",
    "    n=len(x)\n",
    "    sumX, sumY, sumXY, sumXX = 0,0,0,0\n",
    "    for i in range(n):\n",
    "        sumX+=x[i]\n",
    "        sumY+=y[i]\n",
    "        sumXY+=x[i]*y[i]\n",
    "        sumXX+=x[i]**2\n",
    "    a=(n*sumXY-sumX*sumY)/(n*sumXX-sumX*sumX)\n",
    "    b=(sumXX*sumY-sumX*sumXY)/(n*sumXX-sumX*sumX)\n",
    "    return a, b\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "xi = [i for i in range(1,11)]\n",
    "yi=[10,11.5,12,13,14.5,15.5,16.8,17.3, 18, 18.7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y=   0.98606x +    9.30667\n"
     ]
    }
   ],
   "source": [
    "a,b=calcAB(xi,yi)\n",
    "print('y=%10.5fx + %10.5f' % (a,b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=np.linspace(0,10)\n",
    "y=a*x+b\n",
    "plt.plot(x,y)\n",
    "plt.scatter(xi,yi)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "1c836ea1838598e769062a9cf5fa60bfd2e0efc946eee0512711b4e28a236389"
  },
  "kernelspec": {
   "display_name": "Python 3.8.13 ('base')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
